The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification
- Autores
- Palopoli, Nicolás; Iserte, Javier Alonso; Chemes, Lucia Beatriz; Marino Buslje, Cristina; Parisi, Gustavo Daniel; Gibson, Toby James; Davey, N.E.
- Año de publicación
- 2020
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Modern biology produces data at a staggering rate. Yet, much of these biological data is still isolated in the text, figures, tables and supplementary materials of articles. As a result, biological information created at great expense is significantly underutilised. The protein motif biology field does not have sufficient resources to curate the corpus of motif-related literature and, to date, only a fraction of the available articles have been curated. In this study, we develop a set of tools and a web resource, 'articles.ELM', to rapidly identify the motif literature articles pertinent to a researcher's interest. At the core of the resource is a manually curated set of about 8000 motif-related articles. These articles are automatically annotated with a range of relevant biological data allowing in-depth search functionality. Machine-learning article classification is used to group articles based on their similarity to manually curated motif classes in the Eukaryotic Linear Motif resource. Articles can also be manually classified within the resource. The 'articles.ELM' resource permits the rapid and accurate discovery of relevant motif articles thereby improving the visibility of motif literature and simplifying the recovery of valuable biological insights sequestered within scientific articles. Consequently, this web resource removes a critical bottleneck in scientific productivity for the motif biology field.
Fil: Palopoli, Nicolás. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Iserte, Javier Alonso. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: Chemes, Lucia Beatriz. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Marino Buslje, Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: Parisi, Gustavo Daniel. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Gibson, Toby James. Ruprecht Karls Universitat Heidelberg; Alemania
Fil: Davey, N.E.. The Institute of Cancer Research; Reino Unido - Materia
-
LINEAR MOTIF
TEXT MINING
DATABASE
DISCOVERY - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/137873
Ver los metadatos del registro completo
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The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classificationPalopoli, NicolásIserte, Javier AlonsoChemes, Lucia BeatrizMarino Buslje, CristinaParisi, Gustavo DanielGibson, Toby JamesDavey, N.E.LINEAR MOTIFTEXT MININGDATABASEDISCOVERYhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Modern biology produces data at a staggering rate. Yet, much of these biological data is still isolated in the text, figures, tables and supplementary materials of articles. As a result, biological information created at great expense is significantly underutilised. The protein motif biology field does not have sufficient resources to curate the corpus of motif-related literature and, to date, only a fraction of the available articles have been curated. In this study, we develop a set of tools and a web resource, 'articles.ELM', to rapidly identify the motif literature articles pertinent to a researcher's interest. At the core of the resource is a manually curated set of about 8000 motif-related articles. These articles are automatically annotated with a range of relevant biological data allowing in-depth search functionality. Machine-learning article classification is used to group articles based on their similarity to manually curated motif classes in the Eukaryotic Linear Motif resource. Articles can also be manually classified within the resource. The 'articles.ELM' resource permits the rapid and accurate discovery of relevant motif articles thereby improving the visibility of motif literature and simplifying the recovery of valuable biological insights sequestered within scientific articles. Consequently, this web resource removes a critical bottleneck in scientific productivity for the motif biology field.Fil: Palopoli, Nicolás. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Iserte, Javier Alonso. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Chemes, Lucia Beatriz. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Marino Buslje, Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Parisi, Gustavo Daniel. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gibson, Toby James. Ruprecht Karls Universitat Heidelberg; AlemaniaFil: Davey, N.E.. The Institute of Cancer Research; Reino UnidoOxford University Press2020-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/137873Palopoli, Nicolás; Iserte, Javier Alonso; Chemes, Lucia Beatriz; Marino Buslje, Cristina; Parisi, Gustavo Daniel; et al.; The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification; Oxford University Press; Database; 2020; 1-2020; 1-101758-04631758-0463CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/database/article/doi/10.1093/database/baaa040/5850858info:eu-repo/semantics/altIdentifier/doi/10.1093/database/baaa040info:eu-repo/semantics/altIdentifier/url/http://slim.icr.ac.uk/articles/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:47:13Zoai:ri.conicet.gov.ar:11336/137873instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:47:13.605CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification |
title |
The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification |
spellingShingle |
The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification Palopoli, Nicolás LINEAR MOTIF TEXT MINING DATABASE DISCOVERY |
title_short |
The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification |
title_full |
The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification |
title_fullStr |
The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification |
title_full_unstemmed |
The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification |
title_sort |
The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification |
dc.creator.none.fl_str_mv |
Palopoli, Nicolás Iserte, Javier Alonso Chemes, Lucia Beatriz Marino Buslje, Cristina Parisi, Gustavo Daniel Gibson, Toby James Davey, N.E. |
author |
Palopoli, Nicolás |
author_facet |
Palopoli, Nicolás Iserte, Javier Alonso Chemes, Lucia Beatriz Marino Buslje, Cristina Parisi, Gustavo Daniel Gibson, Toby James Davey, N.E. |
author_role |
author |
author2 |
Iserte, Javier Alonso Chemes, Lucia Beatriz Marino Buslje, Cristina Parisi, Gustavo Daniel Gibson, Toby James Davey, N.E. |
author2_role |
author author author author author author |
dc.subject.none.fl_str_mv |
LINEAR MOTIF TEXT MINING DATABASE DISCOVERY |
topic |
LINEAR MOTIF TEXT MINING DATABASE DISCOVERY |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Modern biology produces data at a staggering rate. Yet, much of these biological data is still isolated in the text, figures, tables and supplementary materials of articles. As a result, biological information created at great expense is significantly underutilised. The protein motif biology field does not have sufficient resources to curate the corpus of motif-related literature and, to date, only a fraction of the available articles have been curated. In this study, we develop a set of tools and a web resource, 'articles.ELM', to rapidly identify the motif literature articles pertinent to a researcher's interest. At the core of the resource is a manually curated set of about 8000 motif-related articles. These articles are automatically annotated with a range of relevant biological data allowing in-depth search functionality. Machine-learning article classification is used to group articles based on their similarity to manually curated motif classes in the Eukaryotic Linear Motif resource. Articles can also be manually classified within the resource. The 'articles.ELM' resource permits the rapid and accurate discovery of relevant motif articles thereby improving the visibility of motif literature and simplifying the recovery of valuable biological insights sequestered within scientific articles. Consequently, this web resource removes a critical bottleneck in scientific productivity for the motif biology field. Fil: Palopoli, Nicolás. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Iserte, Javier Alonso. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina Fil: Chemes, Lucia Beatriz. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina Fil: Marino Buslje, Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina Fil: Parisi, Gustavo Daniel. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Gibson, Toby James. Ruprecht Karls Universitat Heidelberg; Alemania Fil: Davey, N.E.. The Institute of Cancer Research; Reino Unido |
description |
Modern biology produces data at a staggering rate. Yet, much of these biological data is still isolated in the text, figures, tables and supplementary materials of articles. As a result, biological information created at great expense is significantly underutilised. The protein motif biology field does not have sufficient resources to curate the corpus of motif-related literature and, to date, only a fraction of the available articles have been curated. In this study, we develop a set of tools and a web resource, 'articles.ELM', to rapidly identify the motif literature articles pertinent to a researcher's interest. At the core of the resource is a manually curated set of about 8000 motif-related articles. These articles are automatically annotated with a range of relevant biological data allowing in-depth search functionality. Machine-learning article classification is used to group articles based on their similarity to manually curated motif classes in the Eukaryotic Linear Motif resource. Articles can also be manually classified within the resource. The 'articles.ELM' resource permits the rapid and accurate discovery of relevant motif articles thereby improving the visibility of motif literature and simplifying the recovery of valuable biological insights sequestered within scientific articles. Consequently, this web resource removes a critical bottleneck in scientific productivity for the motif biology field. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/137873 Palopoli, Nicolás; Iserte, Javier Alonso; Chemes, Lucia Beatriz; Marino Buslje, Cristina; Parisi, Gustavo Daniel; et al.; The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification; Oxford University Press; Database; 2020; 1-2020; 1-10 1758-0463 1758-0463 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/137873 |
identifier_str_mv |
Palopoli, Nicolás; Iserte, Javier Alonso; Chemes, Lucia Beatriz; Marino Buslje, Cristina; Parisi, Gustavo Daniel; et al.; The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification; Oxford University Press; Database; 2020; 1-2020; 1-10 1758-0463 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/database/article/doi/10.1093/database/baaa040/5850858 info:eu-repo/semantics/altIdentifier/doi/10.1093/database/baaa040 info:eu-repo/semantics/altIdentifier/url/http://slim.icr.ac.uk/articles/ |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf application/pdf application/pdf |
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Oxford University Press |
publisher.none.fl_str_mv |
Oxford University Press |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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